Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations19768
Missing cells10929
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory110.0 B

Variable types

Categorical1
Boolean6
Text1
Numeric9
DateTime2

Alerts

label is highly imbalanced (67.2%) Imbalance
type is highly imbalanced (92.8%) Imbalance
site_admin is highly imbalanced (95.8%) Imbalance
log_public_repos has 942 (4.8%) infinite values Infinite
log_public_gists has 7961 (40.3%) infinite values Infinite
log_followers has 1445 (7.3%) infinite values Infinite
log_following has 6017 (30.4%) infinite values Infinite
bio has 10929 (55.3%) missing values Missing
public_repos is highly skewed (γ1 = 53.8847472) Skewed
public_gists is highly skewed (γ1 = 74.09063706) Skewed
followers is highly skewed (γ1 = 32.46602776) Skewed
following is highly skewed (γ1 = 39.87415424) Skewed
public_repos has 942 (4.8%) zeros Zeros
public_gists has 7961 (40.3%) zeros Zeros
followers has 1445 (7.3%) zeros Zeros
following has 6017 (30.4%) zeros Zeros
text_bot_count has 19003 (96.1%) zeros Zeros
log_public_repos has 551 (2.8%) zeros Zeros
log_public_gists has 1873 (9.5%) zeros Zeros
log_followers has 803 (4.1%) zeros Zeros
log_following has 1734 (8.8%) zeros Zeros

Reproduction

Analysis started2024-11-19 15:26:17.036550
Analysis finished2024-11-19 15:26:27.304061
Duration10.27 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

label
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Human
18578 
Bot
 
1190

Length

Max length5
Median length5
Mean length4.8796034
Min length3

Characters and Unicode

Total characters96460
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHuman
2nd rowHuman
3rd rowHuman
4th rowBot
5th rowHuman

Common Values

ValueCountFrequency (%)
Human 18578
94.0%
Bot 1190
 
6.0%

Length

2024-11-19T23:26:27.461173image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T23:26:27.569374image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
human 18578
94.0%
bot 1190
 
6.0%

Most occurring characters

ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

type
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
19597 
False
 
171
ValueCountFrequency (%)
True 19597
99.1%
False 171
 
0.9%
2024-11-19T23:26:27.669423image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

site_admin
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
19678 
True
 
90
ValueCountFrequency (%)
False 19678
99.5%
True 90
 
0.5%
2024-11-19T23:26:27.753750image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

company
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
10794 
False
8974 
ValueCountFrequency (%)
True 10794
54.6%
False 8974
45.4%
2024-11-19T23:26:27.858809image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

blog
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
11256 
True
8512 
ValueCountFrequency (%)
False 11256
56.9%
True 8512
43.1%
2024-11-19T23:26:27.937051image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

location
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
12691 
False
7077 
ValueCountFrequency (%)
True 12691
64.2%
False 7077
35.8%
2024-11-19T23:26:28.036146image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

hireable
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
16470 
True
3298 
ValueCountFrequency (%)
False 16470
83.3%
True 3298
 
16.7%
2024-11-19T23:26:28.134599image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

bio
Text

Missing 

Distinct8641
Distinct (%)97.8%
Missing10929
Missing (%)55.3%
Memory size154.6 KiB
2024-11-19T23:26:28.419358image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length160
Median length116
Mean length61.460459
Min length1

Characters and Unicode

Total characters543249
Distinct characters1746
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8574 ?
Unique (%)97.0%

Sample

1st rowI just press the buttons randomly, and the program evolves...
2nd rowTime is unimportant, only life important.
3rd rowDone studying. Need challenges.
4th rowAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.
5th rowSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.
ValueCountFrequency (%)
3069
 
3.9%
and 2526
 
3.2%
engineer 1583
 
2.0%
software 1521
 
1.9%
of 1488
 
1.9%
at 1380
 
1.8%
developer 1236
 
1.6%
the 1086
 
1.4%
a 1038
 
1.3%
i 1033
 
1.3%
Other values (14754) 62407
79.6%
2024-11-19T23:26:28.888623image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 543249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 543249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 543249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

public_repos
Real number (ℝ)

Skewed  Zeros 

Distinct674
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.139215
Minimum0
Maximum50000
Zeros942
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-19T23:26:29.019534image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median35
Q383
95-th percentile250
Maximum50000
Range50000
Interquartile range (IQR)72

Descriptive statistics

Standard deviation574.75022
Coefficient of variation (CV)6.8309434
Kurtosis3700.1203
Mean84.139215
Median Absolute Deviation (MAD)29
Skewness53.884747
Sum1663264
Variance330337.81
MonotonicityNot monotonic
2024-11-19T23:26:29.153884image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942
 
4.8%
1 551
 
2.8%
2 465
 
2.4%
3 396
 
2.0%
4 380
 
1.9%
6 364
 
1.8%
5 357
 
1.8%
7 330
 
1.7%
9 312
 
1.6%
8 307
 
1.6%
Other values (664) 15364
77.7%
ValueCountFrequency (%)
0 942
4.8%
1 551
2.8%
2 465
2.4%
3 396
2.0%
4 380
1.9%
5 357
 
1.8%
6 364
 
1.8%
7 330
 
1.7%
8 307
 
1.6%
9 312
 
1.6%
ValueCountFrequency (%)
50000 1
< 0.1%
27746 1
< 0.1%
26360 1
< 0.1%
22618 1
< 0.1%
20693 1
< 0.1%
17425 1
< 0.1%
16985 1
< 0.1%
16839 1
< 0.1%
9666 1
< 0.1%
9554 1
< 0.1%

public_gists
Real number (ℝ)

Skewed  Zeros 

Distinct359
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.214083
Minimum0
Maximum55781
Zeros7961
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-19T23:26:29.286158image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile66
Maximum55781
Range55781
Interquartile range (IQR)10

Descriptive statistics

Standard deviation635.69014
Coefficient of variation (CV)25.211709
Kurtosis5955.7935
Mean25.214083
Median Absolute Deviation (MAD)2
Skewness74.090637
Sum498432
Variance404101.96
MonotonicityNot monotonic
2024-11-19T23:26:29.419027image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7961
40.3%
1 1873
 
9.5%
2 1152
 
5.8%
3 823
 
4.2%
4 665
 
3.4%
5 627
 
3.2%
6 488
 
2.5%
7 405
 
2.0%
9 327
 
1.7%
8 318
 
1.6%
Other values (349) 5129
25.9%
ValueCountFrequency (%)
0 7961
40.3%
1 1873
 
9.5%
2 1152
 
5.8%
3 823
 
4.2%
4 665
 
3.4%
5 627
 
3.2%
6 488
 
2.5%
7 405
 
2.0%
8 318
 
1.6%
9 327
 
1.7%
ValueCountFrequency (%)
55781 1
< 0.1%
53660 1
< 0.1%
28943 1
< 0.1%
26879 1
< 0.1%
15482 1
< 0.1%
10604 1
< 0.1%
3450 1
< 0.1%
3170 1
< 0.1%
2565 1
< 0.1%
1750 1
< 0.1%

followers
Real number (ℝ)

Skewed  Zeros 

Distinct1598
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.49702
Minimum0
Maximum95752
Zeros1445
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-19T23:26:29.552943image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median33
Q3125
95-th percentile836
Maximum95752
Range95752
Interquartile range (IQR)118

Descriptive statistics

Standard deviation1535.94
Coefficient of variation (CV)6.2564506
Kurtosis1570.3008
Mean245.49702
Median Absolute Deviation (MAD)31
Skewness32.466028
Sum4852985
Variance2359111.6
MonotonicityNot monotonic
2024-11-19T23:26:29.769408image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1445
 
7.3%
1 803
 
4.1%
2 623
 
3.2%
3 515
 
2.6%
4 450
 
2.3%
5 415
 
2.1%
6 396
 
2.0%
7 347
 
1.8%
8 338
 
1.7%
9 311
 
1.6%
Other values (1588) 14125
71.5%
ValueCountFrequency (%)
0 1445
7.3%
1 803
4.1%
2 623
3.2%
3 515
 
2.6%
4 450
 
2.3%
5 415
 
2.1%
6 396
 
2.0%
7 347
 
1.8%
8 338
 
1.7%
9 311
 
1.6%
ValueCountFrequency (%)
95752 1
< 0.1%
84979 1
< 0.1%
66203 1
< 0.1%
58452 1
< 0.1%
31120 1
< 0.1%
30287 1
< 0.1%
29719 1
< 0.1%
29414 1
< 0.1%
28411 1
< 0.1%
25815 1
< 0.1%

following
Real number (ℝ)

Skewed  Zeros 

Distinct620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.520741
Minimum0
Maximum27775
Zeros6017
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-19T23:26:29.952785image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q322
95-th percentile148
Maximum27775
Range27775
Interquartile range (IQR)22

Descriptive statistics

Standard deviation366.79344
Coefficient of variation (CV)8.2387093
Kurtosis2260.6155
Mean44.520741
Median Absolute Deviation (MAD)4
Skewness39.874154
Sum880086
Variance134537.43
MonotonicityNot monotonic
2024-11-19T23:26:30.086987image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6017
30.4%
1 1734
 
8.8%
2 1092
 
5.5%
3 794
 
4.0%
4 602
 
3.0%
5 533
 
2.7%
6 484
 
2.4%
7 407
 
2.1%
8 368
 
1.9%
9 322
 
1.6%
Other values (610) 7415
37.5%
ValueCountFrequency (%)
0 6017
30.4%
1 1734
 
8.8%
2 1092
 
5.5%
3 794
 
4.0%
4 602
 
3.0%
5 533
 
2.7%
6 484
 
2.4%
7 407
 
2.1%
8 368
 
1.9%
9 322
 
1.6%
ValueCountFrequency (%)
27775 1
< 0.1%
16741 1
< 0.1%
15931 1
< 0.1%
11921 1
< 0.1%
10268 1
< 0.1%
9720 1
< 0.1%
9686 1
< 0.1%
9532 1
< 0.1%
9367 1
< 0.1%
7374 1
< 0.1%
Distinct19767
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Minimum2008-01-27 07:09:47+00:00
Maximum2021-12-20 05:29:41+00:00
2024-11-19T23:26:30.229482image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:30.369587image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct19633
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Minimum2016-08-08 22:18:09+00:00
Maximum2023-10-14 14:33:48+00:00
2024-11-19T23:26:30.519331image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:30.686075image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

text_bot_count
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061361797
Minimum0
Maximum5
Zeros19003
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-19T23:26:30.788547image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34100309
Coefficient of variation (CV)5.5572539
Kurtosis51.672415
Mean0.061361797
Median Absolute Deviation (MAD)0
Skewness6.674794
Sum1213
Variance0.11628311
MonotonicityNot monotonic
2024-11-19T23:26:30.905189image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19003
96.1%
1 425
 
2.1%
2 251
 
1.3%
3 75
 
0.4%
4 9
 
< 0.1%
5 5
 
< 0.1%
ValueCountFrequency (%)
0 19003
96.1%
1 425
 
2.1%
2 251
 
1.3%
3 75
 
0.4%
4 9
 
< 0.1%
5 5
 
< 0.1%
ValueCountFrequency (%)
5 5
 
< 0.1%
4 9
 
< 0.1%
3 75
 
0.4%
2 251
 
1.3%
1 425
 
2.1%
0 19003
96.1%

log_public_repos
Real number (ℝ)

Infinite  Zeros 

Distinct674
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite942
Infinite (%)4.8%
Mean-inf
Minimum-inf
Maximum10.819778
Zeros551
Zeros (%)2.8%
Negative942
Negative (%)4.8%
Memory size154.6 KiB
2024-11-19T23:26:31.019535image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentile0
Q12.3978953
median3.5553481
Q34.4188406
95-th percentile5.5214609
Maximum10.819778
Rangeinf
Interquartile range (IQR)2.0209453

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-inf
Median Absolute Deviation (MAD)0.96644052
Skewnessnan
Sum-inf
Variancenan
MonotonicityNot monotonic
2024-11-19T23:26:31.171171image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-inf 942
 
4.8%
0 551
 
2.8%
0.6931471806 465
 
2.4%
1.098612289 396
 
2.0%
1.386294361 380
 
1.9%
1.791759469 364
 
1.8%
1.609437912 357
 
1.8%
1.945910149 330
 
1.7%
2.197224577 312
 
1.6%
2.079441542 307
 
1.6%
Other values (664) 15364
77.7%
ValueCountFrequency (%)
-inf 942
4.8%
0 551
2.8%
0.6931471806 465
2.4%
1.098612289 396
2.0%
1.386294361 380
1.9%
1.609437912 357
 
1.8%
1.791759469 364
 
1.8%
1.945910149 330
 
1.7%
2.079441542 307
 
1.6%
2.197224577 312
 
1.6%
ValueCountFrequency (%)
10.81977828 1
< 0.1%
10.23084696 1
< 0.1%
10.17960299 1
< 0.1%
10.02650133 1
< 0.1%
9.937550758 1
< 0.1%
9.765661236 1
< 0.1%
9.740085881 1
< 0.1%
9.731452904 1
< 0.1%
9.176369852 1
< 0.1%
9.164715194 1
< 0.1%

log_public_gists
Real number (ℝ)

Infinite  Zeros 

Distinct359
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite7961
Infinite (%)40.3%
Mean-inf
Minimum-inf
Maximum10.929189
Zeros1873
Zeros (%)9.5%
Negative7961
Negative (%)40.3%
Memory size154.6 KiB
2024-11-19T23:26:31.302720image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentilenan
Q1nan
median0.69314718
Q32.3025851
95-th percentile4.1896547
Maximum10.929189
Rangeinf
Interquartile range (IQR)nan

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-inf
Median Absolute Deviation (MAD)2.8622009
Skewnessnan
Sum-inf
Variancenan
MonotonicityNot monotonic
2024-11-19T23:26:31.455351image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-inf 7961
40.3%
0 1873
 
9.5%
0.6931471806 1152
 
5.8%
1.098612289 823
 
4.2%
1.386294361 665
 
3.4%
1.609437912 627
 
3.2%
1.791759469 488
 
2.5%
1.945910149 405
 
2.0%
2.197224577 327
 
1.7%
2.079441542 318
 
1.6%
Other values (349) 5129
25.9%
ValueCountFrequency (%)
-inf 7961
40.3%
0 1873
 
9.5%
0.6931471806 1152
 
5.8%
1.098612289 823
 
4.2%
1.386294361 665
 
3.4%
1.609437912 627
 
3.2%
1.791759469 488
 
2.5%
1.945910149 405
 
2.0%
2.079441542 318
 
1.6%
2.197224577 327
 
1.7%
ValueCountFrequency (%)
10.92918859 1
< 0.1%
10.89042312 1
< 0.1%
10.27308366 1
< 0.1%
10.19910059 1
< 0.1%
9.647433338 1
< 0.1%
9.268986567 1
< 0.1%
8.14612951 1
< 0.1%
8.061486867 1
< 0.1%
7.849713758 1
< 0.1%
7.467371067 1
< 0.1%

log_followers
Real number (ℝ)

Infinite  Zeros 

Distinct1598
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite1445
Infinite (%)7.3%
Mean-inf
Minimum-inf
Maximum11.469517
Zeros803
Zeros (%)4.1%
Negative1445
Negative (%)7.3%
Memory size154.6 KiB
2024-11-19T23:26:31.602626image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentilenan
Q11.9459101
median3.4965076
Q34.8283137
95-th percentile6.7286286
Maximum11.469517
Rangeinf
Interquartile range (IQR)2.8824036

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-inf
Median Absolute Deviation (MAD)1.417066
Skewnessnan
Sum-inf
Variancenan
MonotonicityNot monotonic
2024-11-19T23:26:31.736051image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-inf 1445
 
7.3%
0 803
 
4.1%
0.6931471806 623
 
3.2%
1.098612289 515
 
2.6%
1.386294361 450
 
2.3%
1.609437912 415
 
2.1%
1.791759469 396
 
2.0%
1.945910149 347
 
1.8%
2.079441542 338
 
1.7%
2.197224577 311
 
1.6%
Other values (1588) 14125
71.5%
ValueCountFrequency (%)
-inf 1445
7.3%
0 803
4.1%
0.6931471806 623
3.2%
1.098612289 515
 
2.6%
1.386294361 450
 
2.3%
1.609437912 415
 
2.1%
1.791759469 396
 
2.0%
1.945910149 347
 
1.8%
2.079441542 338
 
1.7%
2.197224577 311
 
1.6%
ValueCountFrequency (%)
11.46951679 1
< 0.1%
11.35015945 1
< 0.1%
11.10048106 1
< 0.1%
10.97596118 1
< 0.1%
10.34560598 1
< 0.1%
10.31847386 1
< 0.1%
10.29954185 1
< 0.1%
10.28922603 1
< 0.1%
10.25453167 1
< 0.1%
10.158711 1
< 0.1%

log_following
Real number (ℝ)

Infinite  Zeros 

Distinct620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite6017
Infinite (%)30.4%
Mean-inf
Minimum-inf
Maximum10.231892
Zeros1734
Zeros (%)8.8%
Negative6017
Negative (%)30.4%
Memory size154.6 KiB
2024-11-19T23:26:31.903649image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentilenan
Q1nan
median1.3862944
Q33.0910425
95-th percentile4.9972123
Maximum10.231892
Rangeinf
Interquartile range (IQR)nan

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-inf
Median Absolute Deviation (MAD)2.0794415
Skewnessnan
Sum-inf
Variancenan
MonotonicityNot monotonic
2024-11-19T23:26:32.036848image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-inf 6017
30.4%
0 1734
 
8.8%
0.6931471806 1092
 
5.5%
1.098612289 794
 
4.0%
1.386294361 602
 
3.0%
1.609437912 533
 
2.7%
1.791759469 484
 
2.4%
1.945910149 407
 
2.1%
2.079441542 368
 
1.9%
2.197224577 322
 
1.6%
Other values (610) 7415
37.5%
ValueCountFrequency (%)
-inf 6017
30.4%
0 1734
 
8.8%
0.6931471806 1092
 
5.5%
1.098612289 794
 
4.0%
1.386294361 602
 
3.0%
1.609437912 533
 
2.7%
1.791759469 484
 
2.4%
1.945910149 407
 
2.1%
2.079441542 368
 
1.9%
2.197224577 322
 
1.6%
ValueCountFrequency (%)
10.23189161 1
< 0.1%
9.725616079 1
< 0.1%
9.676022176 1
< 0.1%
9.38605683 1
< 0.1%
9.236787542 1
< 0.1%
9.181940897 1
< 0.1%
9.178436823 1
< 0.1%
9.162409838 1
< 0.1%
9.144948153 1
< 0.1%
8.905715579 1
< 0.1%

Interactions

2024-11-19T23:26:25.944545image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:17.854655image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.773972image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.643018image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.396076image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.292765image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.290825image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.155344image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.057323image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.039112image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:17.942529image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.873655image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:20.516381image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.492512image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.389267image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.387819image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.252801image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.159751image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.145585image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.036947image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.971291image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:20.608250image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.590662image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.535777image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.489902image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.359286image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.254869image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.240132image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.171514image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.064470image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:20.721367image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.694193image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.622198image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.576641image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.455049image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.361618image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.338733image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.271555image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.167171image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:20.806942image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.788240image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.736749image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.675084image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.560374image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.458272image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.442058image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.376999image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.259133image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:20.910119image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.892336image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.855997image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.769701image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.663648image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.544676image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.542653image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.477099image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.354872image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.061019image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.987932image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.956405image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.871222image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.762662image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.639243image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.638172image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.571562image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.458539image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.187728image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.088165image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.076516image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.973322image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.858819image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.744922image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:26.741854image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:18.671119image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:19.554074image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:21.293594image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:22.189126image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:23.188121image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.071565image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:24.955278image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-19T23:26:25.840456image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Missing values

2024-11-19T23:26:26.888699image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T23:26:27.170393image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
0HumanTrueFalseFalseFalseFalseFalseNaN261512011-09-26 17:27:03+00:002023-10-13 11:21:10+00:0003.2580970.0000001.6094380.000000
1HumanTrueFalseFalseTrueFalseTrueI just press the buttons randomly, and the program evolves...303962015-06-29 10:12:46+00:002023-10-07 06:26:14+00:0003.4011971.0986122.1972251.791759
2HumanTrueFalseTrueTrueTrueTrueTime is unimportant,\nonly life important.1034912122212008-08-29 16:20:03+00:002023-10-02 02:11:21+00:0004.6347293.8918207.1000275.398163
3BotTrueFalseFalseFalseTrueFalseNaN4908422014-05-20 18:43:09+00:002023-10-12 12:54:59+00:0003.891820-inf4.4308170.693147
4HumanTrueFalseFalseFalseFalseTrueNaN111622012-08-16 14:19:13+00:002023-10-06 11:58:41+00:0002.3978950.0000001.7917590.693147
5HumanTrueFalseTrueTrueTrueFalseDone studying. Need challenges.5612272017-04-11 14:08:07+00:002023-10-11 05:59:26+00:0004.0253520.0000003.0910421.945910
6HumanTrueFalseTrueTrueTrueTrueAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.277113963162008-04-07 22:22:22+00:002023-09-27 09:04:56+00:0005.6240187.0379064.1431352.772589
7HumanTrueFalseTrueFalseTrueFalseSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.3712202012-01-19 21:57:07+00:002023-08-07 16:06:34+00:0003.6109180.0000003.091042-inf
8HumanTrueFalseFalseFalseFalseFalseNaN272375962019-12-24 20:04:33+00:002023-10-12 11:55:01+00:0003.2958370.6931473.6109186.390241
9HumanTrueFalseTrueTrueTrueFalseHi4291422013-07-23 23:29:34+00:002023-10-09 20:47:05+00:0003.7376702.1972252.6390570.693147
labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
19758HumanTrueFalseTrueFalseTrueFalseNaN30010112016-09-10 09:45:00+00:002023-10-06 11:30:51+00:0003.401197-inf2.3025852.397895
19759HumanTrueFalseFalseFalseTrueTrueNaN37199162012-04-19 03:27:14+00:002023-10-07 18:13:52+00:0003.6109182.9444394.5108601.791759
19760BotTrueFalseFalseFalseFalseFalseI am the bot account of @alvaroaleman10002018-12-15 19:55:31+00:002021-07-27 14:14:25+00:0020.000000-inf-inf-inf
19761HumanTrueFalseFalseFalseFalseFalseNaN30102013-11-10 16:05:37+00:002023-08-31 14:26:08+00:0021.098612-inf0.000000-inf
19762HumanTrueFalseFalseFalseFalseFalseNaN00002020-10-01 18:30:32+00:002020-12-29 19:45:12+00:000-inf-inf-inf-inf
19763BotTrueFalseTrueTrueTrueFalseTony came to Linux in 1994 and has never looked back. His entire professional career has been spent working with or on Linux. First as a systems administrator36161142014-07-02 23:27:34+00:002023-08-15 16:38:34+00:0003.5835192.7725892.3978951.386294
19764HumanTrueFalseFalseFalseFalseFalseNaN160302017-12-06 21:56:31+00:002023-07-26 18:32:25+00:0002.772589-inf1.098612-inf
19765HumanTrueFalseTrueFalseTrueFalseSoftware engineer at RealTracs.1301012015-11-14 14:44:05+00:002022-08-23 21:09:49+00:0002.564949-inf2.3025850.000000
19766HumanTrueFalseTrueFalseFalseFalseNaN70202021-11-23 18:55:29+00:002023-10-06 22:50:45+00:0001.945910-inf0.693147-inf
19767BotTrueFalseFalseFalseTrueFalseNaN100102016-04-22 22:11:59+00:002022-07-07 19:48:21+00:0002.302585-inf0.000000-inf